Inferring cellular networks – a review

This review gives an overview of computational and statistical methods to reconstruct cellular networks and deals with conditional independence models including Gaussian graphical models and Bayesian networks.

Abstract

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.